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Housing price forecastability: A factor analysis


  • Lasse Bork

    () (Aalborg University)

  • Stig V. Møller

    () (Aarhus University and CREATES)


We examine US housing price forecastability using a common factor approach based on a large panel of 122 economic time series. We find that a simple three-factor model generates an explanatory power of about 50% in one-quarter ahead in-sample forecasting regressions. The predictive power of the model stays high at longer horizons. The estimated factors are strongly statistically signi?cant according to a bootstrap resampling method which takes into account that the factors are estimated regressors. The simple three-factor model also contains substantial out-of-sample predictive power and performs remarkably well compared to both autoregressive benchmarks and computational intensive forecast combination models.

Suggested Citation

  • Lasse Bork & Stig V. Møller, 2012. "Housing price forecastability: A factor analysis," CREATES Research Papers 2012-27, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2012-27

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    Cited by:

    1. repec:ipg:wpaper:2014-585 is not listed on IDEAS
    2. Paul E. Carrillo & Eric R. Wit & William Larson, 2015. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the United States and the Netherlands," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(3), pages 609-651, September.
    3. Theodore Panagiotidis & Panagiotis Printzis, 2016. "On the macroeconomic determinants of the housing market in Greece: a VECM approach," International Economics and Economic Policy, Springer, vol. 13(3), pages 387-409, July.
    4. Paul E. Carrillo & Erik Robert De Wit & William D. Larson, 2012. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the U.S. and the Netherlands," Working Papers 2012-11, The George Washington University, Institute for International Economic Policy.
    5. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.
    6. Charles Rahal, 2015. "House Price Forecasts with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    7. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05r, Department of Economics, University of Birmingham.

    More about this item


    House prices; Forecasting; Factor model; Principal components; Macroeconomic factors; Factor forecast combination; Bootstrap;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • G1 - Financial Economics - - General Financial Markets

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